Peering into the matrix: A look at biofilms and their inherent antibiotic resistance
Bibliographic record
Abstract
Biofilms are increasingly being regarded as the predominant form of bacterial growth in natural settings. These structures consist of bacterial cells immobilized at a surface and encased in a self-produced matrix of extracellular polymeric substances. In clinical settings, biofilms are the cause of persistent infections that are difficult to clear through the action of the host immune system. Biofilm-encased cells are also associated with increased levels of antibiotic resistance compared to their planktonic counterparts. The result is increased morbidity and mortality when biofilms are associated with disease. In this review, the focus of discussion will be the various mechanisms of antibiotic resistance common to biofilms and the role these mechanisms play in the pathogenesis of major clinically-relevant microorganisms. Antibiotic penetration, altered microenvironments, phenotypic variation, and adaptive resistance mechanisms are all key players in the development of antibiotic resistance in bacterial biofilms. Though the relative significance of each individual mechanism varies, when combined they confer extensive protection to the biofilm’s cellular populations. Keywords: biofilms; antibiotic resistance (mechanisms of); clinical applications; disease; antibiotic penetration; altered microenvironments; phenotypic variation; adaptive resistance; review
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".